CS-EV0005 - Special Course on Representing Hierarchies in Hyperbolic Geometry

Hyperbolic geometry is one of three natural geometries (Euclidean, elliptic/spherical, and hyperbolic), which has been intensively studied in mathematics since its inception in the early 19th century. Recently, it has been gaining more and more traction in computer science and machine learning as it is better suited for representing hierarchical data than Euclidean geometry.

The seminar will begin with a short introduction to hyperbolic geometry, as well as a short overview of machine learning techniques in Euclidean spaces; these will build both our intuition and technical skills. The presentations will cover topics including nearest neighbor search, embeddings (especially of trees), Gromov hyperbolicity, hyperbolic dimension reduction, as well as optimization and learning of representations.